让大模型专精辩论任务，既懂专业又不丢通用能力
ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation
- 用105个自然语言指令构建领域专用训练数据
- 在52k条指令上微调后，新任务表现显著提升
- 适合需要逻辑推理与论辩分析的研究者
让大语言模型（LLM）遵循指令的能力显著提升了其处理未见任务的能力。然而，尽管具备强大的泛化能力，指令跟随型LLM在需要领域知识的任务中仍表现不佳。本文针对计算性论辩（Computational Argumentation, CA）领域，提出专门的指令微调方法，旨在使LLM能有效应对任何未见过的CA任务，同时保持其通用能力。基于现有CA研究，我们设计了105个自然语言指令以覆盖各类任务。在此基础上，构建了一个面向LLM的CA专用基准测试，可全面评估其解决不同CA任务的能力。通过自指导（self-instruct）流程合成52,000条与CA相关的指令，训练出一个专注于论辩领域的指令跟随型LLM。实验表明，该方法显著提升了模型在已知和未见CA任务上的表现，同时在SuperNI基准上的通用自然语言任务性能保持稳定。
原文摘要 · Abstract (English)
Training large language models (LLMs) to follow instructions has significantly enhanced their ability to tackle unseen tasks. However, despite their strong generalization capabilities, instruction-following LLMs encounter difficulties when dealing with tasks that require domain knowledge. This work introduces a specialized instruction fine-tuning for the domain of computational argumentation (CA). The goal is to enable an LLM to effectively tackle any unseen CA tasks while preserving its generalization capabilities. Reviewing existing CA research, we crafted natural language instructions for 105 CA tasks to this end. On this basis, we developed a CA-specific benchmark for LLMs that allows for a comprehensive evaluation of LLMs' capabilities in solving various CA tasks. We synthesized 52k CA-related instructions, adapting the self-instruct process to train a CA-specialized instruction-following LLM. Our experiments suggest that CA-specialized instruction fine-tuning significantly enhances the LLM on both seen and unseen CA tasks. At the same time, performance on the general NLP tasks of the SuperNI benchmark remains stable.
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